93GPTQ vs AWQ: how do these post-training quantization methods differ, and when do you pick each?▼hardNVIDIAHugging FaceAWS1 replies◆ premiumBoth compress an LLM to 4-bit weights, yet they choose what to protect in very different ways. What matters is GPTQ's error-correcting solve versus AWQ's activation-aware scaling, and the calibration each demands.Open full answer →
03Explain quantization for inference: INT8/INT4, GPTQ/AWQ, what breaks, and how you validate it.▼hard★ EssentialNVIDIAOpenAIxAI2 repliesunlockedQuantization is the opening move for shrinking and accelerating models, and the interviewer expects more than 'use fewer bits.' What they grade is whether you know what each precision level gains you, why outliers wreck naive quantization, and how you demonstrate quality survived. This lays that out.Open full answer →
